Unsupervised Learning of Object Landmarks via Self-Training Correspondence
Authors: Dimitrios Mallis, Enrique Sanchez, Matthew Bell, Georgios Tzimiropoulos
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This Section presents experiments illustrating the results produced by our method and by recent state-of-the-art approaches, as well as ablation studies shedding light into some of the key properties of our method. Datasets: We validate our approach on faces, human bodies and cat faces. |
| Researcher Affiliation | Collaboration | Dimitrios Mallis University of Nottingham dimitrios.mallis@nottingham.ac.uk Enrique Sanchez Samsung AI Center, Cambridge, UK e.lozano@samsung.com Matt Bell University of Nottingham matt.bell@nottingham.ac.uk Georgios Tzimiropoulos Queen Mary University of London, UK Samsung AI Center, Cambridge, UK g.tzimiropoulos@qmul.ac.uk |
| Pseudocode | No | The paper describes the algorithms and procedures in prose, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/malldimi1/Unsupervised Landmarks. |
| Open Datasets | Yes | Datasets: We validate our approach on faces, human bodies and cat faces. For faces, we used Celeb A [23], AFLW [18], and the challenging LS3D [4]... For human bodies, we use BBCPose [8] and Human3.6M [14]. |
| Dataset Splits | Yes | For AFLW we used the official train/test partitions, and for LS3D we followed the same protocol as [4] and used the 300W-LP partition [53] to train our models. |
| Hardware Specification | No | The paper does not explicitly state any specific hardware used for running the experiments (e.g., GPU model, CPU type, memory). |
| Software Dependencies | No | All models were implemented in Py Torch [28]. ... For K-means, we used the Faiss library [16]. No specific version numbers for PyTorch or Faiss are provided. |
| Experiment Setup | Yes | We used RMSprop [13], with learning rate equal to 5 10 4, weight decay 10 5 and batch-size 16 for stage 1 and 64 for stage 2. We set M = 100 and M = 250 clusters for facial and body landmarks, respectively. ... no more than 300,000 iterations are necessary for the algorithm to converge for all datasets. |